Use operational intelligence to analyze data and gather insights that you can use to improve your business operations.
Your business processes can capture large amounts of business data. But for the data to be meaningful, you must be able to gain actionable insights from that data. You can use operational intelligence to make more informed decisions. By using business rules and incoming event information, you can take better advantage of your organization's data and inject machine learning techniques to optimize processes. Consider every decision that needs to be taken as part of a business process. If you can get a recommendation that is based on the decisions that you made in the past in similar situations, your processes are greatly enhanced.
One example is a recommendation system for an insurance company. The company has a workflow process to approve or reject insurance claims. Some claims are simple and can be handled manually. Other claims are more complex. The approval decision or the decision on which path to follow is a human task. This task is captured as a task in a workflow process. A machine learning algorithm can help to determine which decision to take based on past decisions.
This scenario can be adapted to any kind of human decision process. In the insurance claim example, the decision consists of approving or rejecting a claim: a yes-or-no decision. The recommendation service builds the claim approval process and stores the business process data. It builds the machine learning model and deploys it.
The Operational intelligence architecture highlights the tools, processes, and people that you need to design such a solution.
By adopting operational intelligence, you can optimize your business processes and realize these benefits:
Address operational issues and opportunities as they arise or even before they do, as in predictive maintenance
Make more informed decisions on a day-by-day basis
Increase your visibility and insight into business operations, which can lead to higher revenue and competitive advantages over rivals
IBM Cloud Pak® for Automation includes a platform-level component that provides visual insights to business owners and that feeds a data lake to infuse AI into digital business automation. With IBM Cloud Pak for Automation, you can capture all events that are generated by the operational systems that are implemented with the digital business automation products. You can then aggregate them into business-relevant KPIs and present them in meaningful dashboards for lines of business to have a near real-time view on their business operations.
By using the Cloud Pak, you can take advantage of the valuable data that is generated by those operational systems. You can apply analytics or machine learning algorithms that add intelligence to the platform and provide guidance to knowledge workers and business owners. The data can be anonymized and published in a data lake. AI potential is illustrated in a form that data scientists and developers can reuse.
An Operational intelligence (OI) architecture delivers visibility and insight into an organization's business operations. An organization executes its business operations through a combination of business processes and workflows, repeatable decisions, digitized document content, and software robots. In parallel, key operational information is gathered and analyzed to provide the organization with the ability to make and immediately act upon the analytic insights through manual or automated actions.
An operational intelligence engine consumes operational data in the form of events emitted from the organization's applications. Business metrics and key performance indicators (KPIs) are calculated from the data and stored for visualization on configurable dashboards. Machine learning algorithms can be applied to the operational data collected over time to provide consumable AI services, such as recommendations and next best action.
This architecture supports the following features:
Support for modeling of business metrics and key performance indicators (KPIs) required to provided business insight.
Enable the instrumentation of an organization's applications to emit events containing key business information.
Consume events from multiple sources and systems, and correlate and aggregate the operational information into a business context to provide insight.
Provide configurable dashboard visualization of the business information for reporting to and analysis by business users.
Long-term data lake storage of operational data allows machine learning capabilities to be applied to predict trends. AI services can then aid process improvement by providing recommendations and next best actions.
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